GETebook.ai

TrustMRR

GETebook.ai turns ideas into publish-ready ebooks in minutes. Pick a topic, audience, and tone — our AI generates a structured outline, full chapters and cover art, then exports to PDF, EPUB, and Word with professional typography. Built for course creators, coaches, marketers, and indie authors who want lead magnets, paid products, or KDP-ready manuscripts without the blank-page grind.

Visit product View on TrustMRRJul 29, 2025SaaS B2C stripe

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Actual performance

142customers

$6,452MRR/mo

Made the leaderboard

Traction signals

Domain Rating10

MRR growth 30d+96.5%

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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